Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books
This addresses the problem of predicting book success for authors and publishers, but it is incremental as it applies existing methods to a new domain.
The paper tackled predicting book success by modeling the flow of emotions using recurrent neural networks, achieving a best weighted F1-score of 69% in a multitask setting that also predicts genre.
Books have the power to make us feel happiness, sadness, pain, surprise, or sorrow. An author's dexterity in the use of these emotions captivates readers and makes it difficult for them to put the book down. In this paper, we model the flow of emotions over a book using recurrent neural networks and quantify its usefulness in predicting success in books. We obtained the best weighted F1-score of 69% for predicting books' success in a multitask setting (simultaneously predicting success and genre of books).